Systems and methods for utilizing property features from images

ABSTRACT

A process for locating real estate parcels for a user comprises accessing a library of parceled real estate image data to identify objects and features in a plurality of parcels identified by the user as having a feature of interest. A predictive model is constructed and applied to a geographic region selected by the user to generate a customized output of real estate parcels predicted to have the feature of interest.

INCORPORATION BY REFERENCE

The present patent application claims priority to the provisional patentapplication identified by U.S. Ser. No. 62/354,873, filed on Jun. 27,2016, entitled “System and Method for Machine Learning Based Extractionof Property Information,” the entire content of which is herebyincorporated herein by reference.

BACKGROUND

Online real estate marketing tools and computer software applicationsare increasingly being used to improve and speed the real estateselection process. For example, mobile applications (apps) can provideusers with images, interactive maps, and value estimates for homes inthe provider's database. Other online apps assist users in findingneighborhoods and houses that match the user's search criteria orlocation needs.

Most potential buyers start their home search using one or more onlinereal estate databases. It is common for web sites to display individualdata points in connection with a geographic map. For example, many realestate web sites permit their users to display a map of a particulargeographic region that shows the location of every home that iscurrently for sale. A user may click on one of these homes to obtaindetailed information about the home. Similarly, some property taxjurisdictions make available web sites that show each property taxparcel on a map; a user may click on one of the parcels to see detailedinformation about it. See, for example, U.S. Pat. No. 8,095,434.

It is common for users to query a database by submitting a query thatspecifies values of one or more fields present in the database, andreceive in return a result listing records in the database that containthe specified values in the specified fields. Queries conventionallyinclude geographic data and relational data. Relational database fieldsare typically limited to attributes such as price, number of bedrooms,number of bathrooms, square feet, and the like. In some cases, thequeries also include text data. A query that specifies attributes ofmultiple types, sometimes called a “hybrid query,” can be firstprocessed against an index appropriate to each attribute type resultingin multiple intermediate query results. In order to obtain a final queryresult from the intermediate query results, the intermediate queryresults are joined, or “intersected,” so that the final query resultcontains only data items present in each of the intermediate queryresults. See, for example, U.S. Pat. No. 9,424,305 describing methods toincrease the processing efficiency for a conventional hybrid query.

FIG. 1 is a data flow diagram showing a conventional process forresponding to a hybrid query. A relational Index A provides informationsuch as price, square feet, lot size, and the like. Index B containsgeographic information and Index C contains descriptive terms providedby the seller. The normalized query results from each index areintersected to provide a final query result. FIG. 2 is another exampleflow chart of this conventional process.

Unfortunately, to maintain each index, the information must be enteredat least once by hand. This limits the amount of information that canultimately be searched. While some online real estate databases arequite large, users must still often search multiple databases to findproperties with the desired characteristics. Often the desiredcharacteristic is not apparent or searchable, making even a digitalsearch quite time consuming. In addition, qualitative information suchas the look and feel of the real estate is difficult to properly index,particularly since the provider of this information may havesignificantly different views relative to the user. Accordingly,approaches to creating and using indices that can take into accountqualitative information based on machine learning rather than manualinput would have significant utility.

SUMMARY

A process for locating real estate parcels for a user comprisesaccessing a library of image and other supplemental data that may beparceled by real estate/ownership boundaries to identify objects andfeatures that may be externally discernable or shared in a plurality ofparcels identified by the user as having a feature or characteristic ofinterest. A predictive model of the desired object and features isconstructed and applied to a geographic region selected by the user togenerate a customized output of real estate parcels predicted to havethe feature of interest.

In one embodiment, one or more non-transitory computer readable mediumstore a set of computer executable instructions for running on one ormore computer systems that when executed cause the one or more computersystems to access a library of parceled real estate data includingobject and feature identifications and classifications that may bediscernable through imagery alone or based on geospatially combiningimagery data with parcel-level or other regional information (notnecessarily discernable within the imagery, e.g. school districts, HVACdetails, tax information), to identify and classify objects and featurespresent in one or more real estate parcel(s) selected by a user ashaving a feature of interest, and develop and apply a predictive modelto a geographic region selected by the user to identify real estateparcels predicted to have the same feature of interest. Artificialneural network machine learning algorithms can be used to develop thepredictive model and the object and feature identifications andclassifications by analyzing images taken from one or more orientationsor distances from the real estate parcels (e.g., street view images,satellite and/or aerial images) showing the objects and features. Theimagery may be fused with supplemental parcel information (e.g. numberof stories, heating/cooling systems, livable space, number of rooms) tofurther enhance predictive capabilities through the artificial neuralnetwork machine learning technology. Thus, the limitations of the priorart that do not take into account the look and feel of the real estateparcels when identifying candidate real estate parcels are overcome.

BRIEF DESCRIPTION OF SEVERAL VIEWS OF THE DRAWINGS

To assist those of ordinary skill in the relevant art in making andusing the subject matter hereof, reference is made to the appendeddrawings, which are not intended to be drawn to scale, and in which likereference numerals are intended to refer to similar elements forconsistency. For purposes of clarity, not every component may be labeledin every drawing.

FIG. 1 is a data flow diagram for maintaining and querying aconventional real estate database system.

FIG. 2 is a flow chart of the conventional online real estate selectionprocess of FIG. 1.

FIG. 3 is a flow chart for an example process for locating candidatereal estate parcels for a user in accordance with the presentdisclosure.

FIG. 4 illustrates an example system for locating candidate real estateparcels for a user in accordance with the present disclosure.

FIG. 5 illustrates an example database in the system of FIG. 4.

FIG. 6 is a partial view of a screen from a user's tablet showinguser-identified parcels of interest in the Example.

FIG. 7 shows the model output to the user's tablet in the Example.

DETAILED DESCRIPTION

Before explaining at least one embodiment of the disclosure in detail,it is to be understood that the disclosure is not limited in itsapplication to the details of construction, experiments, exemplary data,and/or the arrangement of the components set forth in the followingdescription or illustrated in the drawings unless otherwise noted.

The disclosure is capable of other embodiments or of being practiced orcarried out in various ways. For example, although the real estateshopping market may be used as an example, feature extraction in one ormore images in other industries, such as insurance, roofing, andconstruction is contemplated. Additionally, identification of featuresabsent within one or more images is also contemplated. Also, it is to beunderstood that the phraseology and terminology employed herein is forpurposes of description, and should not be regarded as limiting.

The following detailed description refers to the accompanying drawings.The same reference numbers in different drawings may identify the sameor similar elements.

As used in the description herein, the terms “comprises,” “comprising,”“includes,” “including,” “has,” “having,” or any other variationsthereof, are intended to cover a non-exclusive inclusion. For example,unless otherwise noted, a process, method, article, or apparatus thatcomprises a list of elements is not necessarily limited to only thoseelements, but may also include other elements not expressly listed orinherent to such process, method, article, or apparatus.

Further, unless expressly stated to the contrary, “or” refers to aninclusive and not to an exclusive “or”. For example, a condition A or Bis satisfied by one of the following: A is true (or present) and B isfalse (or not present), A is false (or not present) and B is true (orpresent), and both A and B are true (or present).

In addition, use of the “a” or “an” are employed to describe elementsand components of the embodiments herein. This is done merely forconvenience and to give a general sense of the inventive concept. Thisdescription should be read to include one or more, and the singular alsoincludes the plural unless it is obvious that it is meant otherwise.Further, use of the term “plurality” is meant to convey “more than one”unless expressly stated to the contrary.

As used herein, any reference to “one embodiment,” “an embodiment,”“some embodiments,” “one example,” “for example,” or “an example” meansthat a particular element, feature, structure or characteristicdescribed in connection with the embodiment is included in at least oneembodiment. The appearance of the phrase “in some embodiments” or “oneexample” in various places in the specification is not necessarily allreferring to the same embodiment, for example.

Circuitry, as used herein, may be analog and/or digital components, orone or more suitably programmed processors (e.g., microprocessors) andassociated hardware and software, or hardwired logic. Also, “components”may perform one or more functions. The term “component,” may includehardware, such as a processor (e.g., microprocessor), an applicationspecific integrated circuit (ASIC), field programmable gate array(FPGA), a combination of hardware and software, and/or the like. Theterm “processor” as used herein means a single processor or multipleprocessors working independently or together to collectively perform atask.

Software may include one or more computer readable instructions thatwhen executed by one or more components cause the component to perform aspecified function. It should be understood that the algorithmsdescribed herein may be stored on one or more non-transitory computerreadable medium. Exemplary non-transitory computer readable mediums mayinclude random access memory, read only memory, flash memory, and/or thelike. Such non-transitory computer readable mediums may be electricallybased, optically based, and/or the like.

Referring now to FIG. 3, shown therein is a flow chart 10 of anexemplary process for automatically locating real estate parcels for auser. In step 12, information is received from a user regarding one ormore specific address(es) or real estate parcel(s) having a feature ofinterest to the user. A library of parceled real estate image and othersupplemental data is accessed in step 14 and the objects and featuresbounded by the parcel are identified in step 16. Based on the identifiedobjects and features in the parcel(s) selected by the user, a customizedpredictive model is constructed in step 20. The customized predictivemodel is applied to a geographic region in step 22 to identifyadditional real estate parcels predicted to have the feature ofinterest. In step 24 a customized output of the identified real estateparcels is generated.

The library of parceled real estate data is not limited to externallyvisible or discernable objects and features. There can also be otherproperty attributes that pertain to the parcel (supplemental data), suchas number of stories, heating system, etc., that are not visible but canstill be tied to the property and aggregated with the imagery data.

Exemplary goals of the presently-disclosed inventive concepts are to (1)provide robust deep-learning/machine-learning classifiers for parcellevel trending on-the-fly, based on a set of user-supplied locations viaan interface, such as a web-like interface or a smartphone interface;(2) apply classifiers to imagery and supplemental data of a desiredregion of interest, such as a town, a municipality, a metro area, astate, a nation, or worldwide, to create predictive feature models; and(3) provide the user suggestions, profiling, and information based onapplication of the predictive feature models to larger regions ofinterest set by the user.

In an exemplary embodiment, the user supplies “crowd-sourced”information to aid in predictive model generation. Identified propertieshaving features of interest are utilized to builddeep-learning/machine-learning custom classifiers or models on a smallsubset of data that are then deployed to a larger region. Crowd-sourcedinformation can be part of the supplemental information such as nearbyrunning trails, coffee shops, neighborhood related details, publictransit, quality of school district, etc. The crowd-sourced data caninclude anything that can be geospatially associated with a propertyaddress.

Large-scale deployment facilitates identification of parcels orlocations with similar characteristics to those originally selected bythe user.

In one embodiment, the information regarding the real estate parcels ofinterest is uploaded by the user from a computer. The term “computer”includes a personal computer, a smart phone, a network-capable TV set, aTV set-top box, a tablet, an e-book reader, a laptop computer, a desktopcomputer, a network-capable handheld device, a video game console, andthe like.

There exist various nationwide and to some degree globally recognizedproperty ownership boundaries. These boundaries go through a qualityassurance (QA) process and are typically provided by large companieslike Pitney Bowes Inc. and CoreLogic, Inc. These boundaries are oftengathered from county- or city-level surveys and compiled/aggregated bythese companies. The boundaries are then used to intersect theimagery/property information in a geospatial (latitude/longitude)context.

The user can merely like the look and feel of a particular real estateparcel, or the user may be interested in specific objects, features orcombinations thereof in a particular parcel. Examples of the look of theparticular real estate parcel may include the style of the property,e.g., art deco, bungalow, cape cod, colonial, contemporary, craftsman,dutch colonial, federal, french provincial, georgian, gothic revival,greek revival, prairie, pueblo, queen anne, ranch, regency, saltbox,second empire, shed, shingle, shotgun, spanish eclectic, split level,stick, tudor and victorian. Examples of the look of the particular realestate parcel may include an arrangement or the relative size of objectson the property. The term “feel” as used herein means an emotion (e.g.,love, hate) or sensation (excitement, loathing) induced by viewing thereal estate parcel or one or more images showing the real estate parcel.In either case, using a computer, the user identifies the one or moreparcels by physical address, or the user can identify the parcels ofinterest from a map. For example, the user may be provided a parceledmap with satellite images of the properties and allowed to view thesatellite images of the properties and select one or more parcels ofinterest by, for example, clicking on region(s) of the satellite imagesshowing the one or more parcels of interest.

The library of parceled real estate image and supplemental data accessedin step 14 can be created using remote sensing technologies thatcollect, process and store image data in a database. The image data canbe captured with a sensor (e.g., a camera). The sensor can be orientedand located in various orientations, such as streetview, satelliteand/or aircraft-based sensors and may contain nominal ‘visible-band’(red, green, blue) data or other spectral bands (e.g. infrared). Suchsensors can provide images which can then be used to detect and measureobjects and structures within the images. The remote sensingtechnologies may also include a monitoring system (e.g., a GlobalPositioning System and/or Inertial Measurement Unit) that collects andlogs geolocation metadata that relates the sensor data (e.g., images) toparticular locations on the Earth. See, for example, U.S. Pat. No.7,424,133 that describes techniques for geolocating oblique images andmeasuring within the oblique images. The entire content of U.S. Pat. No.7,424,133 is hereby incorporated herein by reference. Also, see U.S.Publication No. 2015/0347872 describing object detection from aerialimages using disparity mapping and segmentation techniques. Techniquesknown in the art as “bundle adjustment” can also be used to createand/or enhance the geolocation data. The geolocation data can be storedas metadata within the images, or stored separately from the images andrelated to the images using any suitable technique, such as uniqueidentifiers.

The parcel boundaries can be derived from GIS (geographic informationsystem) coordinates of a piece of property. In the United States, legalproperty boundaries are specified on deeds and subdivision maps whichare recorded at the recorder's office for the jurisdiction in which theland lies and are public information. Local tax assessors use therecorded documents to maintain tax maps. Tax assessor maps, oftenavailable from the assessors' websites, have parcel information that isindexed by address and provided a unique assessor parcel number (APN) orequivalent. These maps are public information, often in a GIS formatsuch as shapefiles or geojsons. Shapefiles and the like could bevisualized as a “cookie cutter” because indicia indicative of the parcelboundary(ies) can be placed on top of the image (e.g., overlaid) or setof images through geolocation and the image data (pixel data andspectral band information) may be clipped to these boundaries (likeusing a cookie cutter). In one embodiment, the image pixels within aparticular parcel boundary can be analyzed without clipping the imagedata to the parcel boundary(ies). Then one can know which pixels in theimage pertain to which parcel region. The “cookie cutter” can stamp outany number of images from any date, time, or sensor assuming the imagehas been geolocated, i.e., made into latitude/longitude points so thateach pixel corresponds to a point on the earth.

Images comprise unstructured data. Despite the fact that the image maybe georeferenced, the information in the pixels of the image are notcatalogued or ordered in a way to provide contextual or searchablemeaning until objects and features, etc. are associated with groups ofpixels and tagged with a property/geolocation.

Physical information includes the general location of objects andfeatures in the property. Semantic information includes contextualrelationships, for example, “pool in a backyard,” “driveway to thegarage,” etc. Both the physical information as it exists in the image (amap of the labeled objects for instance) and the contextualrelationships these objects may have in conjunction with theirsurroundings can be preserved in the database.

Other techniques are available for analyzing images and extractingfeatures from images. See, for example, U.S. Pat. No. 9,082,162describing methods for image searching using manual input,classification and/or segmentation. Such methods allow a user to selecta portion of an image of an object, and additional searching focuses onthe selected part.

Another example, PCT International Publication No. WO2016/054694describes accessing geographical information system (GIS) data,including land-parcel data, to identify appropriate sites for potentialproperty development. Geographical information system (GIS) data isaccessed, including land-parcel data representing land-parcelcoordinates that define land parcels. Other methods and systems areknown to those skilled in the art for coordinating land parcelinformation with satellite and aircraft-based imagery data.

In step 16, objects and features in the selected real estate parcels areidentified using the library of parceled real estate data. This data isanalyzed to create a new classifier or model predictive of the user'sdesired features. In one embodiment, the user has selected multipleparcels having several features of interest. The objects and features(e.g., a pool, an arrangement of trees, style of a house, separategarage, or the like) in these multiple parcels are analyzed usingmachine learning algorithms to determine most likely common features anda predictive model is constructed in step 18 based on these identifiedfeatures. The machine learning algorithms, often neural network orartificial intelligence based, develop correlations based on imagespectral information, texture information, and other contextual detailsthrough the supply of representative data (e.g. example parcels withfeatures of interest). These correlations are stored as a model that maythen be applied to a broader area of interest, beyond the example parcelset.

Objects of interest may have multiple features. For example, an objectmay be cataloged by size, shape, color, spatial relation to anotherobject, etc. While it is common for real estate listings to include theexistence of a pool on a property, the presently described methods andsystems distinguish pool characteristics such as tile color and distancefrom a lawn.

In one embodiment, algorithms comprising a neural network are utilizedto determine patterns in the features and objects in the selected realestate parcels, and the predictive model is constructed therefrom. Asmentioned above, the network establishes correlations across spectral,spatial, and contextual space for an object or feature of interest. Aset of representative data that contains the objects/features ofinterest can be identified (‘labeled’) as truth data. A percentage ofthis truth data can be submitted to the network for training. Anotherpercent can be reserved for testing the accuracy of the correlationsidentified (the ‘model’). Training entails a statistical method toiterate the application of the correlations or model, ‘learned’ from thetraining data to the test data set. The accuracy of the prediction basedon the known labels can be provided per iteration until a desiredaccuracy is achieved (nominally, >85%, but adjustable depending on theinformation provided) or timeframe is met. The final model postiteration may then be applied to a broader, unlabeled or unconstrained,region. During the training process, features and objects or interestmay be weighted by the end-user based on personal significance ordesire.

In one embodiment, deep learning neural networks classify the featuresand objects in the selected real estate parcels to construct thepredictive model (‘positive representations’). ‘Negativerepresentations’, or representations that do not share the features ofinterest, may be predicted instead, based on what the user would electto omit from desired properties. Such classification recognizesinstances where the user has selected real estate parcels based on alack of, rather than presence of, a specific feature.

Classic examples of a predictive model include a Support Vector Machine(svm) or k-means model. The artificial intelligence/neural networkoutput is a similar type model, but with greater adaptability to bothidentify context and respond to changes in imagery parameters. It istypically a binary output, formatted and dictated by the language/formatof network used that may then be implemented in a separate workflow andapplied for predictive classification to the broader area of interest.

Once the predictive model is generated for a user, the user may bequeried regarding a geographic area of interest. The user may identifyand submit the geographic region of interest using methods known tothose in the art. For example, the user can select or click an area froma map or a dropdown box, or identify a city and state, zip code, and thelike. Once selected, the customized predictive model can be applied, ona parcel by parcel basis, to parcels in the selected geographic regionto generate a customized output of identified or candidate real estateparcels as in steps 22 and 24.

Referring now to FIG. 4, an example computing system 26 is shown foridentifying real estate parcels predicted to have a feature of interestcustomized for a user. The system 26 includes a processor 28, memory 30,and a communication component 32. The memory 30 stores a database 34 andprogram logic 36. The computing system 26 bi-directionally communicateswith a plurality of user devices 38 via a network 40.

Although FIG. 4 illustrates the system 26 as having a single processor28, it should be noted that the system 26 may include multipleprocessors 28. The processor or multiple processors 28 may or may notnecessarily be located in a single physical location.

The database 34 is shown in more detail in FIG. 5 and includes raw imagedata 42, parcel data 44, parceled image data 46, and other data 48. Theraw image data 42 can include, but is not limited to, street-viewimagery, satellite imagery and/or aerial imagery. The parcel data 44includes geographically divided portions of the land and may be providedby government agencies or public utilities, for example. Thegeographically divided portions can include country, state, county,township, and city or individual land owner borders. For purposes hereinof identifying parcels having a feature of interest for a user,individual land owner borders or “real estate parcels” are utilized andincluded in the parcel data 38. Larger parcel data are useful foridentifying regions of interest to the user.

The raw image data 42 and parcel data 44 are further processed by theprogram logic 36 to identify objects and features within each realestate parcel, the objects and features stored with the parceled imagedata 46. It is understood that “other” data 48 can be combined with theparceled image data. Non-limiting examples of other data 48 includeweather, tax appraisals, legal status, and the like.

The program logic 36 can identify the objects and features within eachreal estate parcel using the techniques described above, as well asusing artificial intelligence, such as neural network machine learningalgorithms. In some embodiments, the program logic 36 is adapted toanalyze the images and identify information with respect to objects onthe real estate parcel, the style of the property, an arrangement or therelative size of objects on the property. The program logic 36 can alsobe configured to request from a user information indicative of the“feel” (love, hate, loathing, excitement, etc.) of the parcel and relatethe feel to the identified objects and features within each real estateparcel.

In some embodiments, the program logic 36 is configured to analyze theimages to determine a particular set of outdoor attributes of the realestate parcel, such as parcel square footage, ratios of lawn area, treearea, garden area, concrete area, building area, porch area, manmadearea (e.g., a summation of building area and concrete area) home areaand the like. Example ratios include a lawn area to tree area ratio, alawn area to home area ratio, a lawn area to concrete area ratio, a lawnarea to garden area ratio, a manmade area to parcel area, and a porcharea to garden area ratio.

In one embodiment, a non-transitory computer-readable storage mediumstores program logic 36, e.g., a set of instructions capable of beingexecuted by one or more processor 28, that when executed by the one ormore processor 28 causes the one or more processor 28 to (1) access alibrary of parceled real estate image data including object and featureidentifications and classifications stored in the database 34, andidentify and classify objects and features present in one or more realestate parcel(s) selected by a user as having a feature of interest; (2)use artificial neural network machine learning algorithms to develop apredictive model for identifying other real estate parcels having theuser's feature of interest; and (3) apply the predictive model to ageographic region selected by the user to identify real estate parcelspredicted to have the feature of interest.

In one embodiment, the network 40 is the Internet and the user devices38 interface with the system 26 via the communication component 32 and aseries of web pages. It should be noted, however, that the network 40may be almost any type of network and may be implemented as the WorldWide Web (or Internet), a local area network (LAN), a wide area network(WAN), a metropolitan network, a wireless network, a cellular network, aGlobal System for Mobile Communications (GSM) network, a code divisionmultiple access (CDMA) network, a 3G network, a 4G network, a satellitenetwork, a radio network, an optical network, a cable network, a publicswitched telephone network, an Ethernet network, combinations thereof,and/or the like. It is conceivable that in the near future, embodimentsof the present disclosure may use more advanced networking topologies.

In one embodiment, the system 26 comprises a server system havingmultiple servers in a configuration suitable to provide a commercialcomputer based business system such as a commercial web-site and/or datacenter,

In order to further illustrate the present invention, the followingexample is given. However, it is to be understood that the example isfor illustrative purposes only and is not to be construed as limitingthe scope of the invention.

EXAMPLE

A potential home buyer uses a user device 38, e.g., a tablet, to accessa web-based real estate service provider (system 26) offering theservices described above. The user is moving to another state and wouldlike to find a house with a lap pool with no trees adjacent to the pool.

Referring now to FIG. 6, the user requests a map 50 of the state (e.g.,region of interest) from the system 26, which supplies data indicativeof the map 50 to the tablet. In this example, the data indicative of themap may conform to the requirements of HTML. The tablet receives thedata indicative of the map 50. The tablet renders the data onto a screenof the tablet, thereby displaying the map 50 of the state.

The user interacts with the tablet to cause the tablet to pan into anarea the user is familiar with. This region of interest is shown withparcel boundaries 60 a, 60 b, and 60 c overlaid onto the map 50 and theuser either knows of, or views online, images of a number of theproperties in this region. All of the parcel boundaries 60 have not beenspecifically identified in FIG. 6 or FIG. 7 for purposes of clarity.

The user interactively selects or uploads the addresses for those realestate parcels having a lap pool and no nearby trees and identifies suchreal estate parcels as positive examples 66 a, 66 b and 66 c. Realestate parcels 68 that the user knows do not have a lap pool or havetrees next to the lap pool are identified as negative by the user andare designated with the reference numerals 68, for example.

The real estate service provider obtains the imagery for each of thepositive examples 66 and negative examples 68 and this information isingested into a backend neural network and machine learning algorithmsfor training the system 26 to identify positive and negativecorrelations among these real estate parcels. These correlations resultin a predictive model that is used to predict the existence of thedesired feature (lap pool without adjacent trees) over a differentimagery set, namely the city and state to which the user will be moving.

The user selects the new region of interest. While the new region ofinterest is shown as the same region in FIG. 7, it could also be withinthe original specified region or could include other states, cities,countries, etc.

The final product shown in FIG. 7 is map 70 that shows a predictiveapplication of this predictive model in which candidate real estateparcels 72 are identified that are expected to have the desired featurewith confidence and metrics based on the prediction. Exemplary candidatereal estate parcels 72 a, 72 b, 72 c and 72 d are designated by way ofexample. The candidate real estate parcels 72 a, 72 b, 72 c and 72 d canbe identified, by way of example, by highlighting the parcels Theresults can be displayed on the screen of the tablet as the map 70 or asa list of addresses (not shown).

From the above description and examples, it is clear that the inventiveconcepts disclosed and claimed herein are well adapted to attain theadvantages mentioned herein. While exemplary embodiments of theinventive concepts have been described for purposes of this disclosure,it will be understood that numerous changes may be made which willreadily suggest themselves to those skilled in the art and which areaccomplished within the spirit of the inventive concepts disclosed andclaimed herein.

What is claimed is:
 1. A process for locating parcels for a user, the process comprising the steps of: receiving, from a user, a parcel selection of multiple real estate parcels identified by the user, the multiple real estate parcels including one or more features of interest to the user, wherein the one or more features of interest includes one or more user-desired characteristic associated with each real estate parcel, the one or more features of interest capable of being visually discernable through first imagery of the multiple real estate parcels; accessing, with one or more computer processors, first real estate image data, including the first imagery having pixels and including supplemental data having property attributes, for the parcel selection received from the user, from a library of real estate image data, the library arranged on a parcel by parcel basis; constructing, with the one or more computer processors, a customized predictive model by analyzing the first real estate image data, including at least the first imagery, corresponding to the parcel selection for the one or more features of interest to the user to determine commonalities of the multiple real estate parcels of the parcel selection, the customized predictive model comprising machine learning algorithms used in one or more neural networks that develop and store correlations for the first imagery based on one or more of: image spectral information, image texture information, image contextual details, and patterns in the first real estate image data; receiving, from the user, a selection of a geographic area; identifying one or more real estate parcels predicted by the customized predictive model to have the one or more features of interest of the parcel selection from the user by: analyzing, with the one or more computer processors, second real estate image data, including second imagery and including supplemental data having property attributes, of additional real estate parcels within the geographic area from the library of real estate image data, with the customized predictive model comprising the machine learning algorithms used in the one or more neural networks; and generating a customized output of the identified real estate parcels predicted to have the one or more features of interest.
 2. The process of claim 1, wherein the first real estate image data and the second real estate image data comprise at least one of aerial imagery and satellite imagery.
 3. The process of claim 1, wherein receiving from the user the parcel selection includes receiving physical addresses of the multiple real estate parcels from a computer.
 4. The process of claim 1, wherein receiving from the user the parcel selection includes receiving input by the user from a map.
 5. The process of claim 1, further comprising generating a second customized output of the identified real estate parcels predicted to not have the one or more features of interest.
 6. The process of claim 1, wherein the customized output of the identified real estate parcels is displayed as a map.
 7. The process of claim 1, wherein the customized output of the identified real estate parcels is displayed as a list of addresses.
 8. A system, comprising: a non-transitory computer-readable storage medium storing instructions that, when executed by one or more processors, cause the one or more processors to: receive, from a user, a parcel selection of multiple real estate parcels identified by a user, the multiple real estate parcels including one or more features of interest to the user, wherein the one or more features of interest includes one or more user-desired characteristic associated with each real estate parcel, the one or more features of interest capable of being visually discernable through first imagery of the multiple real estate parcels; access first real estate image data, including first imagery having pixels and including supplemental data having property attributes, for the received parcel selection of multiple real estate parcels having the one or more features of interest to the user, from a library of real estate image data, the library arranged on a parcel by parcel basis; construct a customized predictive model by analyzing the first real estate image data including at least the first imagery, corresponding to the parcel selection for the one or more features of interest to the user to determine commonalities of the multiple real estate parcels of the parcel selection, the customized predictive model comprising machine learning algorithms used in one or more neural networks that develop and store correlations for the first imagery based on one or more of: image spectral information, image texture information, image contextual details, and patterns in the first real estate image data; identify one or more real estate parcels predicted to have the one or more features of interest of the parcel selection from the user by: analyzing second real estate image data, including second imagery and including supplemental data having property attributes, of additional real estate parcels within a geographic area from the library of real estate image data, with the customized predictive model comprising the machine learning algorithms used in the one or more neural networks; and generate a customized output of the identified real estate parcels predicted to have the one or more features of interest.
 9. The system of claim 8, wherein the first real estate image data and the second real estate image data comprise at least one of aerial imagery and satellite imagery.
 10. The system of claim 8, wherein the received parcel selection includes physical addresses of the multiple real estate parcels from a computer.
 11. The system of claim 8, wherein the received parcel selection includes selections from a map.
 12. The system of claim 8, the non-transitory computer-readable storage medium storing instructions that, when executed by one or more processor, further cause the one or more processor to: generate a second customized output of the identified real estate parcels predicted to not have the one or more features of interest.
 13. The system of claim 8, wherein the customized output of the identified real estate parcels is displayed as a map.
 14. The system of claim 8, wherein the customized output of the identified real estate parcels is displayed as a list of addresses. 